Overview

Dataset statistics

Number of variables13
Number of observations731
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.4 KiB
Average record size in memory104.2 B

Variable types

Numeric8
Categorical5

Alerts

mnth is highly correlated with season and 3 other fieldsHigh correlation
season is highly correlated with mnth and 3 other fieldsHigh correlation
weathersit is highly correlated with humHigh correlation
temp is highly correlated with mnth and 3 other fieldsHigh correlation
atemp is highly correlated with mnth and 3 other fieldsHigh correlation
hum is highly correlated with weathersit and 1 other fieldsHigh correlation
rentals is highly correlated with mnth and 4 other fieldsHigh correlation
workingday is highly correlated with weekday and 1 other fieldsHigh correlation
weekday is highly correlated with workingdayHigh correlation
windspeed is highly correlated with humHigh correlation
weekday has 105 (14.4%) zeros Zeros

Reproduction

Analysis started2022-09-20 11:51:22.165614
Analysis finished2022-09-20 11:52:20.526444
Duration58.36 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

day
Real number (ℝ≥0)

Distinct31
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73871409
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:20.930927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.809948796
Coefficient of variation (CV)0.5597629353
Kurtosis-1.194863701
Mean15.73871409
Median Absolute Deviation (MAD)8
Skewness0.006007875846
Sum11505
Variance77.6151978
MonotonicityNot monotonic
2022-09-20T13:52:21.455096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
124
 
3.3%
224
 
3.3%
2824
 
3.3%
2724
 
3.3%
2624
 
3.3%
2524
 
3.3%
2424
 
3.3%
2324
 
3.3%
2224
 
3.3%
2124
 
3.3%
Other values (21)491
67.2%
ValueCountFrequency (%)
124
3.3%
224
3.3%
324
3.3%
424
3.3%
524
3.3%
624
3.3%
724
3.3%
824
3.3%
924
3.3%
1024
3.3%
ValueCountFrequency (%)
3114
1.9%
3022
3.0%
2923
3.1%
2824
3.3%
2724
3.3%
2624
3.3%
2524
3.3%
2424
3.3%
2324
3.3%
2224
3.3%

mnth
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.519835841
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:21.919301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.451912787
Coefficient of variation (CV)0.5294478069
Kurtosis-1.20911201
Mean6.519835841
Median Absolute Deviation (MAD)3
Skewness-0.008148650127
Sum4766
Variance11.91570189
MonotonicityNot monotonic
2022-09-20T13:52:22.292740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
162
8.5%
362
8.5%
562
8.5%
762
8.5%
862
8.5%
1062
8.5%
1262
8.5%
460
8.2%
660
8.2%
960
8.2%
Other values (2)117
16.0%
ValueCountFrequency (%)
162
8.5%
257
7.8%
362
8.5%
460
8.2%
562
8.5%
660
8.2%
762
8.5%
862
8.5%
960
8.2%
1062
8.5%
ValueCountFrequency (%)
1262
8.5%
1160
8.2%
1062
8.5%
960
8.2%
862
8.5%
762
8.5%
660
8.2%
562
8.5%
460
8.2%
362
8.5%

year
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2012
366 
2011
365 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2924
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2012366
50.1%
2011365
49.9%

Length

2022-09-20T13:52:23.069542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T13:52:23.505200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2012366
50.1%
2011365
49.9%

Most occurring characters

ValueCountFrequency (%)
21097
37.5%
11096
37.5%
0731
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2924
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21097
37.5%
11096
37.5%
0731
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common2924
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21097
37.5%
11096
37.5%
0731
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21097
37.5%
11096
37.5%
0731
25.0%

season
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
3
188 
2
184 
1
181 
4
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Length

2022-09-20T13:52:23.831514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T13:52:24.233744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring characters

ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

holiday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
0
710 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Length

2022-09-20T13:52:24.580728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T13:52:24.936985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

weekday
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.997264022
Minimum0
Maximum6
Zeros105
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:25.206475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.004786918
Coefficient of variation (CV)0.6688723127
Kurtosis-1.254282352
Mean2.997264022
Median Absolute Deviation (MAD)2
Skewness0.002741597663
Sum2191
Variance4.019170586
MonotonicityNot monotonic
2022-09-20T13:52:25.535112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6105
14.4%
0105
14.4%
1105
14.4%
2104
14.2%
3104
14.2%
4104
14.2%
5104
14.2%
ValueCountFrequency (%)
0105
14.4%
1105
14.4%
2104
14.2%
3104
14.2%
4104
14.2%
5104
14.2%
6105
14.4%
ValueCountFrequency (%)
6105
14.4%
5104
14.2%
4104
14.2%
3104
14.2%
2104
14.2%
1105
14.4%
0105
14.4%

workingday
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
500 
0
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Length

2022-09-20T13:52:25.921563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T13:52:26.287328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring characters

ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
68.4%
0231
31.6%

weathersit
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
463 
2
247 
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Length

2022-09-20T13:52:26.638230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T13:52:26.994736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct499
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4953847885
Minimum0.0591304
Maximum0.861667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:27.443460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0591304
5-th percentile0.2135685
Q10.3370835
median0.498333
Q30.6554165
95-th percentile0.76875
Maximum0.861667
Range0.8025366
Interquartile range (IQR)0.318333

Descriptive statistics

Standard deviation0.1830509961
Coefficient of variation (CV)0.3695127512
Kurtosis-1.118864155
Mean0.4953847885
Median Absolute Deviation (MAD)0.158333
Skewness-0.05452096476
Sum362.1262804
Variance0.03350766718
MonotonicityNot monotonic
2022-09-20T13:52:27.905029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6355
 
0.7%
0.2658335
 
0.7%
0.684
 
0.5%
0.7108334
 
0.5%
0.5641674
 
0.5%
0.4841674
 
0.5%
0.6491674
 
0.5%
0.6966674
 
0.5%
0.43754
 
0.5%
0.6066673
 
0.4%
Other values (489)690
94.4%
ValueCountFrequency (%)
0.05913041
0.1%
0.09652171
0.1%
0.09739131
0.1%
0.10751
0.1%
0.12751
0.1%
0.1347831
0.1%
0.1383331
0.1%
0.1443481
0.1%
0.151
0.1%
0.1508331
0.1%
ValueCountFrequency (%)
0.8616671
0.1%
0.8491671
0.1%
0.8483331
0.1%
0.8383331
0.1%
0.8341671
0.1%
0.831
0.1%
0.8283331
0.1%
0.82751
0.1%
0.82251
0.1%
0.8183331
0.1%

atemp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct690
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4743539886
Minimum0.0790696
Maximum0.840896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:28.587398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0790696
5-th percentile0.2206455
Q10.3378425
median0.486733
Q30.608602
95-th percentile0.714967
Maximum0.840896
Range0.7618264
Interquartile range (IQR)0.2707595

Descriptive statistics

Standard deviation0.1629611784
Coefficient of variation (CV)0.3435433922
Kurtosis-0.9851305305
Mean0.4743539886
Median Absolute Deviation (MAD)0.135624
Skewness-0.1310880421
Sum346.7527657
Variance0.02655634566
MonotonicityNot monotonic
2022-09-20T13:52:29.433608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6546884
 
0.5%
0.3756213
 
0.4%
0.6370083
 
0.4%
0.5719752
 
0.3%
0.4665252
 
0.3%
0.6079622
 
0.3%
0.6540422
 
0.3%
0.325752
 
0.3%
0.5953462
 
0.3%
0.398352
 
0.3%
Other values (680)707
96.7%
ValueCountFrequency (%)
0.07906961
0.1%
0.09883911
0.1%
0.1016581
0.1%
0.1161751
0.1%
0.117931
0.1%
0.1193371
0.1%
0.1262751
0.1%
0.1442831
0.1%
0.1495481
0.1%
0.1508831
0.1%
ValueCountFrequency (%)
0.8408961
0.1%
0.8263711
0.1%
0.8049131
0.1%
0.8042871
0.1%
0.7948291
0.1%
0.7903961
0.1%
0.7866131
0.1%
0.7859671
0.1%
0.7613671
0.1%
0.7575791
0.1%

hum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct595
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6278940629
Minimum0
Maximum0.9725
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:30.389141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4074545
Q10.52
median0.626667
Q30.7302085
95-th percentile0.8685415
Maximum0.9725
Range0.9725
Interquartile range (IQR)0.2102085

Descriptive statistics

Standard deviation0.1424290951
Coefficient of variation (CV)0.2268361871
Kurtosis-0.06453013469
Mean0.6278940629
Median Absolute Deviation (MAD)0.104584
Skewness-0.06978343399
Sum458.99056
Variance0.02028604714
MonotonicityNot monotonic
2022-09-20T13:52:31.159654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6133334
 
0.5%
0.6053
 
0.4%
0.593
 
0.4%
0.5383333
 
0.4%
0.693
 
0.4%
0.573
 
0.4%
0.5683333
 
0.4%
0.7229173
 
0.4%
0.5520833
 
0.4%
0.741253
 
0.4%
Other values (585)700
95.8%
ValueCountFrequency (%)
01
0.1%
0.1879171
0.1%
0.2541671
0.1%
0.2758331
0.1%
0.291
0.1%
0.3021741
0.1%
0.3051
0.1%
0.311251
0.1%
0.3141671
0.1%
0.3143481
0.1%
ValueCountFrequency (%)
0.97251
0.1%
0.9704171
0.1%
0.96251
0.1%
0.9495831
0.1%
0.9482611
0.1%
0.9395651
0.1%
0.931
0.1%
0.9291671
0.1%
0.9251
0.1%
0.92251
0.1%

windspeed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct650
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1904862116
Minimum0.0223917
Maximum0.507463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:31.854141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0223917
5-th percentile0.07961665
Q10.13495
median0.180975
Q30.2332145
95-th percentile0.343283
Maximum0.507463
Range0.4850713
Interquartile range (IQR)0.0982645

Descriptive statistics

Standard deviation0.07749787068
Coefficient of variation (CV)0.4068424167
Kurtosis0.4109222677
Mean0.1904862116
Median Absolute Deviation (MAD)0.049129
Skewness0.6773454211
Sum139.2454207
Variance0.00600591996
MonotonicityNot monotonic
2022-09-20T13:52:32.578150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1349543
 
0.4%
0.2288583
 
0.4%
0.1368173
 
0.4%
0.11073
 
0.4%
0.1187923
 
0.4%
0.1498833
 
0.4%
0.1679123
 
0.4%
0.1666673
 
0.4%
0.106353
 
0.4%
0.1809752
 
0.3%
Other values (640)702
96.0%
ValueCountFrequency (%)
0.02239171
0.1%
0.04230421
0.1%
0.04540421
0.1%
0.04540831
0.1%
0.046651
0.1%
0.0472751
0.1%
0.05037921
0.1%
0.05287081
0.1%
0.0532131
0.1%
0.0572251
0.1%
ValueCountFrequency (%)
0.5074631
0.1%
0.4415631
0.1%
0.4222751
0.1%
0.4216421
0.1%
0.4179081
0.1%
0.4154291
0.1%
0.41481
0.1%
0.4092121
0.1%
0.4073461
0.1%
0.3980081
0.1%

rentals
Real number (ℝ≥0)

HIGH CORRELATION

Distinct606
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848.1764706
Minimum2
Maximum3410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-09-20T13:52:33.601960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile88
Q1315.5
median713
Q31096
95-th percentile2355
Maximum3410
Range3408
Interquartile range (IQR)780.5

Descriptive statistics

Standard deviation686.6224883
Coefficient of variation (CV)0.8095278661
Kurtosis1.322074327
Mean848.1764706
Median Absolute Deviation (MAD)396
Skewness1.266454032
Sum620017
Variance471450.4414
MonotonicityNot monotonic
2022-09-20T13:52:34.372288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1204
 
0.5%
9684
 
0.5%
1633
 
0.4%
6533
 
0.4%
1233
 
0.4%
1403
 
0.4%
2443
 
0.4%
6393
 
0.4%
7753
 
0.4%
11982
 
0.3%
Other values (596)700
95.8%
ValueCountFrequency (%)
21
0.1%
92
0.3%
151
0.1%
251
0.1%
341
0.1%
382
0.3%
411
0.1%
421
0.1%
431
0.1%
461
0.1%
ValueCountFrequency (%)
34101
0.1%
32831
0.1%
32521
0.1%
31601
0.1%
31551
0.1%
30651
0.1%
30311
0.1%
29631
0.1%
28551
0.1%
28461
0.1%

Interactions

2022-09-20T13:52:14.232621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:38.468342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:43.565541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:49.127827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:54.448584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:59.765265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:04.033291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:09.661268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:14.890785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:39.102586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:44.317529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:49.772256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:55.140720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:00.334077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:04.680206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:10.239118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:15.377696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:39.640363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:44.984444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:50.342998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:55.874967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:00.882374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:05.327432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:10.754881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:15.888141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:40.311687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:45.641773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:50.914166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:56.645441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:01.349741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:06.014973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:11.283611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:16.304695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:40.987766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:46.361790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:51.462497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:57.345266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:01.912724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:06.706040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:11.690223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:16.684319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:41.692433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:47.091225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:52.055626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:58.058004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:02.443781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:07.390895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:12.195522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:17.132356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:42.331248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:47.721669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:52.641896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:58.665229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:02.968897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:07.994368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:12.816636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:17.700465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:42.939236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:48.423099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:53.316610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:51:59.246685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:03.394639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:08.580532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-20T13:52:13.472754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-20T13:52:34.755477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-20T13:52:35.336988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-20T13:52:35.989238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-20T13:52:36.577968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-20T13:52:37.109270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-20T13:52:18.622553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-20T13:52:20.032601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

daymnthyearseasonholidayweekdayworkingdayweathersittempatemphumwindspeedrentals
0112011106020.3441670.3636250.8058330.160446331
1212011100020.3634780.3537390.6960870.248539131
2312011101110.1963640.1894050.4372730.248309120
3412011102110.2000000.2121220.5904350.160296108
4512011103110.2269570.2292700.4369570.18690082
5612011104110.2043480.2332090.5182610.08956588
6712011105120.1965220.2088390.4986960.168726148
7812011106020.1650000.1622540.5358330.26680468
8912011100010.1383330.1161750.4341670.36195054
91012011101110.1508330.1508880.4829170.22326741

Last rows

daymnthyearseasonholidayweekdayworkingdayweathersittempatemphumwindspeedrentals
72122122012106010.2658330.2361130.4412500.407346205
72223122012100010.2458330.2594710.5154170.133083408
72324122012101120.2313040.2589000.7913040.077230174
72425122012112020.2913040.2944650.7347830.168726440
72526122012103130.2433330.2203330.8233330.3165469
72627122012104120.2541670.2266420.6529170.350133247
72728122012105120.2533330.2550460.5900000.155471644
72829122012106020.2533330.2424000.7529170.124383159
72930122012100010.2558330.2317000.4833330.350754364
73031122012101120.2158330.2234870.5775000.154846439